FlatworldEdge used Hadoop technology for information augmentation and Neo4j for graph-based analytics to model and visualize transaction data, which helped in the discovery of new key accounts.

The client's challenge was to identify potential leads with limited third-party data to expand their assets base.

The solution led to efficient data usage, lower customer acquisition costs, and better insights into customer activity.

Story of the Client 

The customer is a leading corporate bank in the Middle East, providing a variety of financial services to a diverse set of clients.The bank faced challenges in identifying high-value transaction parties of their key customer accounts, due to limited data on these third parties.

The bank sought to identify these parties and approach them as potential leads, aiming to expand their asset base and acquire new customers.

The Challenge 

  • The client was struggling to identify high-value transactions and third parties involved due to limited data availability.
  • The need was to visualize complex customer data and transaction patterns to unearth potential leads for customer acquisition.
  • The goal was to make efficient use of current data assets for generating leads and acquiring customers, while keeping the operations cost-effective and feasible.

The Solution 

  • Used Hadoop and Neo4j for information augmentation and graph-based analytics, helping discover new key accounts.
  • Utilized graph visualization to display detailed customer information for better understanding of potential leads.
  • FlatworldEdge enabled efficient usage of data, low cost of customer acquisition, and improved competitiveness.

The Result 

  • Hadoop and Neo4j enhanced data utilization and visualization, deepening customer insights.
  • The approach identified key accounts and leads, fostering cost-effective customer acquisition.
  • FlatworldEdge enabled rapid insights from existing transactions, promoting competitiveness and operational efficiency.